• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.240-244
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• 1992

A new method for generation of binary random sequences, called random sampling method, has been proposed by the authors. However, the random sampling method has the defect that binary random sequence can not be rapidly generated. In this paper, two methods based on the random sampling method are proposed for fast generation of binary random sequences. The optimum conditions for obtaining ideal binary random sequences are derived.

• Journal of Astronomy and Space Sciences
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• v.29 no.2
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• pp.123-129
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• 2012

The BINSYN program package, recently expanded to calculate synthetic spectra of cataclysmic variables, is being further extended to include synthetic photometry of ordinary binary stars in addition to binary stars with optically thick accretion disks. The package includes a capability for differentials correction optimization of eclipsing binary systems using synthetic photometry.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.245-246
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• 1996

The extensive close binary research program earring out at High Altitude Maydanak Observatory (Uzbekistan) by means of the UBVR photoelectric photometry on 1.0 m and 0.6 m Zeiss telescopes is described. It includes more than 240 close binary systems (CBS) in 89 different stellar aggregates. Lightcurves of CBS as well as their orbital elements derived by us are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2304-2308
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• 2003

Ocean thermal energy conversion (OTEC) is an effective method of power generation, which has a small impact on the environment and can be utilized semi-permanently. This paper describes a dynamic model for a pilot OTEC plant built by the Institute of Ocean Energy, Saga University, Japan. This plant is based on Uehara cycle, in which binary mixtures of ammonia and water is used as the working fluid. Some simulation results attained by this model and the analysis of the results are presented. The developed computer simulation can be used to actual practice effectively, such as stable control in a steady operation, optimal determination of the plant specifications for a higher thermal efficiency and evaluation of the economic prospects and off-line training for the operators of OTEC plant.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.488-494
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• 2008

This paper proposes an effective approach based on binary coding Particle Swarm Optimization (PSO) to identify the switching operation plan for feeder reconfiguration. The proposed method considers the advantages and disadvantages of existing particle swarm optimization method and redefined the operators of PSO algorithm to fit the application field of distribution systems. Shift operator is proposed to construct the binary coding particle swarm optimization for feeder reconfiguration. A typical distribution system of Taiwan Power Company is used in this paper to demonstrate the effectiveness of the proposed method. The test results show that the proposed method can apply to feeder reconfiguration problems more effectively and stably than existing method.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.70-74
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• 2006

The performance of conventional direct sequence code division multiple access (DS-CDMA) systems is decreased under environments such as additive white Gaussian noise (AWGN), channel distortion and interference noise due to multiple access user. By means of this parameter, auto correlation value of pseudo noise spreading sequence is decreased at receiver. This techniques which are based on correlation of between signature waveform signal. In this paper, to improve correlation property, we proposed the binary chirp DS-CDMA techniques which combine the DS-CDMA and chirp modulation. The proposed system which is based on binary chirp symbol has a good correlation value. Thus, we called BC DS-CDMA. To evaluate the system's performance, we compare the performance of the proposed systems with DS-CDMA systems under AWGN channel and halogen noise which exists on the powerline. The simulation results show that the proposed method has better performance than conventional technique.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

• Korean Journal of Optics and Photonics
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• v.12 no.3
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• pp.165-171
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• 2001

We discuss the influence of annular binary filters on the axial PSF of imaging systems which are apodized by a radiallysymmetric filter and have spherical aberration and coma. In this paper we consider a whole family of annular binary fIlters in two cases. First, the binary filters are composed of two transparent annuli of the same area. Second, the binary filters are composed of a central clear circle and a concentric annular aperture with area bigger than that of the inner circle. In order to investigate the influence of the proposed filters on the axial PSF, we may use the axial resolution gains, which evaluate in terms of the FWHM of the intensity in the focal region of an apodized imaging system in comparison with that corresponding to a nonapodized one. We evaluate the PSF for the conventional and confocal systems having an aberration. ation.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.357-362
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• 2005

Iris recognition system is the one of the most reliable biometries recognition system. An algorithm is proposed to determine the localized iris from the iris image received from iris input camera in client. For the first step, the algorithm determines the center of pupil. For the second step, the algorithm determines the outer boundary of the iris and the pupillary boundary. The localized iris area is transformed into polar coordinates. After performing three times Wavelet transformation, normalization was done using a sigmoid function. The converting binary process performs normalized value of pixel from 0 to 255 to be binary value, and then the converting binary process is compared pairs of two adjacent pixels. The binary code of the iris is transmitted to the server by the network. In the server, the comparing process compares the binary value of presented iris to the reference value in the database. The process of recognition or rejection is dependent on the value of Hamming Distance. After matching the binary value of presented iris with the database stored in the server, the result is transmitted to the client.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.98-103
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• 2000

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.